Isbn: 9783110680676 - data science in social research: an applied guide to statistical and computational methods with r (7 Ergebnisse)

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Buch. Zustand: Neu. Neuware - Society is changing it is becoming more diverse and digital. Social media today plays a central role in human communication. With a changing society, the way social scientists analyze it is also evolving. In recent years, it has become significantly more difficult to encourage people to participate in surveys. Furthermore, digitization opens up data options that go beyond the survey method. Information published online represents valuable digital behavioral traces and provides social researchers with another important source of data alongside their scientific surveys, which in recent years have increasingly been conducted online.Computational methods have played a central role in social research from the very beginning. This applies today more than ever to data analysis, but now also to data collection. The increasing attention paid to machine learning methods in the statistical analysis of social science data represents a further remarkable development in the analysis of social science data.This textbook addresses these developments and familiarizes readers with both elementary and more advanced methods of data analysis. Fundamentals and techniques of data management, programming with R, statistical data analysis, descriptive and causal inference, as well as predictive modeling are covered in depth. All methods are exemplified using real data either from survey research, the social media platform Bluesky or a large digital newspaper archive. Thematically, these data are focused on current sociological topics, particularly those related to human happiness, energy transition and climate policy, AI, political attitudes and the rise in right-wing voting.Data science encompasses more than the algorithms required for data analysis and statistical learning. No less relevant are the rules by which social research and data analysis are conducted, data quality is ensured, and the results are validated. This textbook aims to provide this overview.…

Buch. Zustand: Neu. Data Science in Social Research | An Applied Guide to Statistical and Computational Methods with R | Uwe Engel (u. a.) | Buch | XI | Englisch | 2026 | Walter de Gruyter | EAN 9783110680676 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu.…

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 467 | Sprache: Englisch | Produktart: Bücher | The emergent field of CSS is growing in scientific importance and public interest. There is no doubt that the field of social science needs to keep up with a rapidly changing society; the ubiquity of digital media in all aspects of life, work, play, and leisure is requiring new perspectives and theories. Driving forces include technological advancements in computer systems, the rise of machine learning, and the ubiq-uity of artificial intelligence. Digital media are being adopted on a large scale globally, leading to the creation and collection of vast amounts of structured and unstructured data. This holds the promise of a new type of open science, where data in the social sciences is a public good instead of serving commercial interests. These developments affect both the objects of social-science research as well as its methods. We are witnessing a paradigm shift from traditional survey research to large-scale computational methods, not least because of the mere availability of large amount of digital trace data. This shift has concrete implications such as usage of disparate kinds of data (e.g., unstructured data), application of new types of methods (e.g., machine learning), exposition to a wide range of threats to data quality and valida-tion, and finally different inferential paradigms. The goal of this textbook is to respond to this paradigm shift with an exposition of computational methods required to conduct social research in the digital age. Five basic points characterize the in-tended scope and focus of the textbook: Selection of methods, Data quality, Statistical learning & validation, Artificial intelligence and Balance of statistical foundations and programming.…